Microelectromechanical accelerometer based sensor system

ABSTRACT

This invention relates to apparatus and systems for providing home and building security and condition monitoring. More particularly, the invention relates to a plurality of devices, including intelligent, multi-sensing, network-connected devices, that communicate dynamically with each other and a remote server.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 14/970,744 entitled “MICROELECTROMECHANICAL ACCELEROMETER BASEDSENSOR SYSTEM” filed on Dec. 16, 2015. The entire content of thatapplication is incorporated herein by reference.

BACKGROUND

The use of portable electronic devices has grown exponentially recently,and in particular, the use of monitoring devices in sporting, health andwork areas to measure activity levels, as well measuring environmentalor user parameters such as temperature, heart rate, altitude, etc. hasincreased substantially. Sometimes, data related to user activities maybe acquired from multiple devices, such as a smart phone, a GPS (GlobalPositioning System) device, a pedometer, a heart rate monitor, etc.

Microelectromechanical systems (MEMS) technology such as accelerometersfor measuring acceleration and gyroscopes for measuring angular velocityhave been implemented within several related devices and applications.For example, individual accelerometer and gyroscope based sensors arecurrently being used in mobile phones, gaming consoles, digital cameras,etc.

MEMS devices generally are capable of producing one or more analogoutput signals that correspond to a given measurement and, therefore, ananalog-to-digital converter (ADC) is usually required to convert theanalog output signals into corresponding digital signals for digitalsignal processing. Applications that include a MEMS device and an ADC,typically implement multi-chip board level technology to couple the MEMSdevice to the ADC, and/or implement the MEMS device and the ADC onseparate chips, printed circuit boards (PCBs), or modules.

These devices enable the capture and collection of data that may be usedin a variety of applications. It would be desirable to utilize thesedevices and information in more efficient and impactful applications.

SUMMARY

The present invention in some embodiments relates to amicro-electro-mechanical-system (MEMS) based sensor system for detectingand analyzing activity levels comprising a wireless router having atransceiver operable to communicate with at least one server programmedto operate as a world wide web server and having a network data adapterto communicate with one or more third party networks; and a wearablemicroelectromechanical sensor configured to connect to a mobileelectronic device, the microelectromechanical sensor including awireless communication transceiver provided internal to themicroelectromechanical sensor wherein the microelectromechanical sensorwirelessly communicates with the mobile electronic device; the mobileelectronic device configured to wirelessly receive and display useractivity data collected by the microelectromechanical sensor, the mobileelectronic device having a transceiver for transmitting the receiveduser activity data to a remote server system device via a communicationsnetwork, the remote server system configured to analyze the useractivity data to determine an associated activity classificationassociated with the user activity data, each activity classificationcorresponding to a predetermined condition and an associated conditionbenefit, the mobile electronic device enabled to transmit instructionsto microelectromechanical sensor in order to vary a sampling conditionof the microelectromechanical sensor in response to the determinedactivity classification.

In other embodiments, the present invention relates to amicroelectromechanical sensor based system for measuring activity levelscomprising at least one wearable microelectromechanical sensorconfigured within a mobile electronic device, the microelectromechanicalsensor including a wireless communication transceiver provided internalto the microelectromechanical sensor for transmitting the measured useractivity data to a remote server system device via a communicationsnetwork, the remote server system configured to analyze the useractivity data to determine an associated activity classificationassociated with the user activity data, each activity classificationcorresponding to a predetermined condition and an associated conditionbenefit, wherein the determined activity classification determines acontent of one or more electronic communications transmitted by theremote server system to a user device.

In further embodiments, the present invention related to amicroelectromechanical sensor based system comprising an accelerometerbased sensor configured to connect to a mobile electronic device, theaccelerometer based sensor including a wireless communicationtransceiver provided internal to the accelerometer sensor wherein theaccelerometer based sensor wirelessly communicates with the mobileelectronic device; the mobile electronic device configured to wirelesslyreceive user activity data collected by the accelerometer based sensor,the mobile electronic device having a transceiver for transmitting thereceived user activity data to a remote server system device via acommunications network, the received user activity data triggering apricing parameter adjustment associated with the user, the remote serversystem configured to analyze the user activity data to determine anassociated activity classification associated with the user activitydata, the mobile electronic device enabled to transmit instructions tothe accelerometer based sensor in order to validate the determinedactivity classification.

BRIEF DESCRIPTION OF THE DRAWINGS

A more detailed understanding may be had from the following description,given by way of example in conjunction with the accompanying drawingswherein:

FIG. 1 shows an exemplary system architecture of the present invention;

FIG. 2 shows an exemplary system that may be used for the management andanalysis of sensor data;

FIG. 3 shows another exemplary system of the present invention;

FIG. 4 shows another exemplary system embodiment of the presentinvention;

FIG. 5 shows a process flow diagram of an exemplary computer implementedmethod in accordance with the present invention;

FIG. 6 shows an exemplary database structure of an embodiment of thepresent invention;

FIG. 7 shows another process flow diagram of an exemplary computerimplemented method in accordance with the present invention; and

FIG. 8 shows another exemplary system of the present invention.

DETAILED DESCRIPTION

Disclosed herein are apparatuses and computing systems to provide aMicroelectromechanical Systems (MEMS) device and interface for businessand the like, wherein the device and interface is provided for useridentification and activity verification that communicates in accordancewith various protocol standards. The device and interface can acceptdata from one or more activity detections sensors and can also usevarious combinations of sensors, thereby providing a high level ofaccuracy and integrity. The device and interface is preferably housedinternally or externally as part of a portable device, such as a mobiletelephone. The device and interface of the present invention providesmutual communication between the user and a third party server forprocessing and evaluation in accordance with various protocol standards.There is thus provided in accordance with a preferred embodiment of thepresent invention a device and interface including an input interfaceoperative to receive activity data signals, an output componentoperative to convert the data signals into converted signals suitablefor transmission to a wireless router, in accordance with any protocolstandard selected from a plurality of standards that define how awireless signal is transmitted from the device to a service providernetwork, the wireless router including transmitter in communication withthe output component operative to transmit to a third party server.

Referring to FIG. 1, a dynamic sensor system 100 for mobile devices 102and 104 configured according to exemplary embodiments, and havingactivity monitoring and wireless positioning capability, will bedescribed. Dynamic sensor system 100 may include one or more differenttypes of activity monitoring components and systems such as MEMS basedaccelerometer and acceleration sensing systems, wireless communicationsystems and/or wireless positioning systems. Dynamic sensor system 100may be configured to in accordance with embodiments of the presentinvention to collect, detect and/or measure one or more of the followingtypes of activity information: pulse, heart rate, pulse rate, breathingrate, blood flow, metabolism, electrolyte type and/or concentration,physical activity, caloric intake, caloric metabolism, blood pH level,physical and/or psychological stress levels and/or stress levelindicators, position and/or balance, body strain, neurologicalfunctioning, brain activity, brain waves, blood pressure, temperature,eye muscle movement, blood volume, inhaled and/or exhaled breath volume,physical exertion, exhaled breath physical and/or chemical composition,psychological mood, sleep patterns, hunger and/or thirst, hormone typeand/or concentration, cholesterol, lipids, blood panel, bone density,organ and/or body weight, reflex response, sexual arousal, mental and/orphysical alertness, sleepiness, voice characteristics, voice tone, voicepitch, voice volume, vital signs, head tilt, allergic reactions,inflammation response, auto-immune response, mutagenic response, DNA,proteins, protein level, digestive system functioning and/or otherphysiological information.

Wireless signals from satellites or GNSS sources may be used fortrilateration of mobile devices 102 and 104. One or more terrestrialsources, such as cellular base stations, Wide Area Network WirelessAccess Points (WAN-WAPs), wide area wireless network (WWAN), WiMAX(e.g., 802.16), etc. may be used for wireless voice and/or datacommunication, and as another source of activity and positioninformation for mobile devices 102 and 104. One or more other wirelesssignal sources such as Local Area Network Wireless Access Points(LAN-WAPs), WLAN, Wi-Fi networks (802.11x), cells, Bluetooth Networks,etc. which may be used for wireless voice and/or data communication, aswell as yet another source for activity and positioning data. Devices102 and 104 may operate in outdoor and indoor environments, such asbuildings, and capable of performing communications over smallergeographic regions than a WWAN, for example. Mobile device 102 and 104may detect activity type information as well as derive positioninformation from any one or more of sources can conform to other typesof positioning.

A Wireless Local Area Network (WLAN) transceiver, router or base station110 may be connected to one or more networks 120 for communicating withand/or detecting signals to/from mobile device 102 and 104. The wirelessrouter 100 may include a wireless transceiver for accessing a wirelesspacket data channels and/or a network interface for coupling to anInternet Protocol (IP) based network. The network interface preferablyincludes a conventional short range wireless transceiver, such as aBluetooth transceiver, a Home RF transceiver, a wireless IP 801.11transceiver, and/or ETSI HyperLANx transceiver for coupling to network120, such as a WLAN. The network interface can include a wiredtransceiver, such as a Fast Ethernet transceiver, or a modem, forinterconnecting with a wired Local Area Network (LAN).

Network 120 may also be connected to the one or more antennas forreceiving satellite or radio frequency signals. Wireless base station110 may provide connectivity via network 120 to one or more third partyservers 160 and 170.

Referring still to FIG. 1, mobile devices 102 and 104 may have one ormore incorporated motion/activity sensors which may be coupled toprocessor to provide movement, position, activity and/or orientationinformation. Mobile devices 102 and 104 may capture and output movement,position, activity and/or orientation data such as activity data 112 and114 associated with devices 102 and 104. By way of example, amotion/activity sensor may utilize motion sensors such as anaccelerometer such as a MEMS device, a gyroscope, a geomagnetic sensorsuch as a compass, an altimeter such as a barometric pressure altimeter,and/or any other type of movement detection sensor. In exemplaryembodiments, motion sensor may utilize one or more components orfeatures derived from motion sensors, such as, a pedometer to detect anumber of walking/step counts, and/or a motion detector toclassify/detect motion modes such as sedentary, running, jumping,walking, etc. Moreover, motion sensor may include a plurality ofdifferent types of devices and combine their outputs in order to providemotion information. For example, the motion sensor may use a combinationof a multi-axis accelerometer and orientation sensors to provide theability to compute positions in two dimensional and/or three dimensionalcoordinate systems. In the present invention, activity data 112 and 114may be captured and via device 102 and 104 and transmitted via router110 through network 120 to servers 160 and 170 for validation,processing and analysis to determine if the activity levels detected areconsistent with predictive or anticipated levels for the user.

FIG. 2 shows an example system architecture 200 that may be used foractivity and motion sensor capture and analysis in accordance with thepresent invention. FIG. 2 shows an example activity sensor computingdevice 210 that may be used to implement features describe above formanaging building and risk data in accordance with embodiments of thepresent invention. Computing device 210 may be integrated as part of orcompletely integrated with an individual computing mobile device 230associated with a user 234 in some embodiments.

Referring still to FIG. 2, the computing device 210 may include aperipheral device interface 212, a display device interface 214, anavigation/location component 216, a processor 218, a memory device 220,a communication interface 222, a data storage 224 and an activity/motiondetector component 226. In operation, computing device 210 is configuredto receive and transmit a number of signals via communications interface222 including, for example, data, image, sound and/or video data relatedto one or more user's activity levels.

Processor 218 may include one or more microprocessors, microcontrollers,and/or digital signal processors that provide processing functions, aswell as other calculation and control functionality. Processor 218 mayinclude any form of logic suitable for performing the processes andinstructions provided herein. For example, the processor 218 may beoperatively configurable based on instructions in the memory 220 toselectively initiate one or more routines that capture and analyzemotion/activity data.

The peripheral device interface 212 may be an interface configured tocommunicate with one or more peripheral devices such as a variety ofsensors, device, cameras and modules. The peripheral device interface212 may operate using a technology such as Universal Serial Bus (USB),PS/2, Bluetooth, infrared, firewire, serial port, parallel port, and/orother appropriate technology. Additionally, the peripheral deviceinterface 212 may, for example, receive input data from an input devicesuch as a keyboard, a mouse, a trackball, a touch screen, a touch pad, astylus pad, and/or other device. Alternatively or additionally, theperipheral device interface 212 may communicate output data to a printerthat is attached to the computing device 210 via the peripheral deviceinterface 212.

The display device interface 214 may be an interface configured tocommunicate data to display device 234. The display device 234 may be,for example, a monitor or television display, a plasma display, a liquidcrystal display (LCD), and/or a display based on a technology such asfront or rear projection, light emitting diodes (LEDs), organiclight-emitting diodes (OLEDs), or Digital Light Processing (DLP). Thedisplay device interface 214 may operate using technology such as VideoGraphics Array (VGA), Super VGA (S-VGA), Digital Visual Interface (DVI),High-Definition Multimedia Interface (HDMI), or other appropriatetechnology. The display device interface 214 may communicate displaydata from the processor 218 to the display device 214 for display by thedisplay device 234. As shown in FIG. 2, the display device 214 may beexternal to the computing device 210, and coupled to the computingdevice 210 via the display device interface 214.

The memory device 220 of FIG. 1 may be or include a device such as aDynamic Random Access Memory (D-RAM), Static RAM (S-RAM), or other RAMor a flash memory. The storage device 224 may be or include a hard disk,a magneto-optical medium, an optical medium such as a CD-ROM, a digitalversatile disk (DVDs), or Blu-Ray disc (BD), or other type of device forelectronic data storage.

The communication interface 222 may be, for example, a communicationsport, a wired transceiver, a wireless transceiver, and/or a networkcard. The communication interface 222 may be capable of communicatingusing technologies such as Ethernet, fiber optics, microwave, xDSL(Digital Subscriber Line), Wireless Local Area Network (WLAN)technology, wireless cellular technology, and/or any other appropriatetechnology. Communications interface may provide connectivity to and/orfrom mobile device 230.

Mobile device 230 may include user display interface 240 which providessuitable interface systems, such as microphone/speaker, keypad, anddisplay that allows user interaction with computing device 210. As usedherein, mobile device 230 may be any portable or movable wireless deviceor machine that is configurable to acquire wireless signals transmittedfrom, and transmit wireless signals to, one or more wirelesscommunication devices or networks. As used herein, the term “wirelessdevice” may refer to any type of wireless communication device which maytransfer information over a network and also have activity, positiondetermination and/or navigation functionality. The wireless device maybe any cellular mobile terminal, personal communication system (PCS)device, personal navigation device, laptop, personal digital assistant,or any other suitable mobile device capable of receiving and processingnetwork and/or GNSS signals such as via wireless network 260.

Wireless network 260 may be implemented using any wireless datatransmission including but not limited to TDMA, GSM, CDPD, GPRS, EDGE,and UMTS.

In a preferred embodiment, a data communications link layer isimplemented using one of these technologies, a data communicationsnetwork layer is implemented with the Internet Protocol (“IP”), and adata communications transmission layer is implemented using theTransmission Control Protocol (“TCP”). In such systems, informationbetween the devices and control device 230 are transmitted using anapplication-level protocol such as, for example, the HyperTextTransmission Protocol (“HTTP”), the Wireless Application Protocol(“WAP”), the Handheld Device Transmission Protocol (“HDTP”), or anyother data communications protocol as will occur to those of skill inthe art.

As used herein, “TDMA” stands for Time Division Multiple Access, atechnology for delivering digital wireless service using time-divisionmultiplexing. “GSM” stands for Global System for Mobile Communications,a digital cellular standard in Europe and Asia. “CDPD” stands forCellular Digital Packet Data, a data transmission technology developedfor use on cellular phone frequencies. “GPRS” stands for General PacketRadio Service, a standard for wireless data communications that supportsa wide range of speeds, is an efficient use of limited bandwidth and isparticularly suited for sending and receiving small bursts of data, suchas e-mail and Web browsing, as well as large volumes of data. “EDGE”stands for Enhanced Data Rates for GSM Evolution, a standard forwireless data communications supporting high data transfer rates. “UMTS”stands for Universal Mobile Telecommunication System, a standard forwireless data communications supporting high data transfer rates andalso referred to as W-CDMA for Wideband Code Division Multiple Access.

Alternatively or additionally, an instance of the computing device 210may be configured to perform any feature or any combination of featuresdescribed herein. In such an instance, the memory device 220 and/or thestorage device 224 may store instructions which, when executed by theprocessor 218, cause the processor 218 to perform any feature or anycombination of features described herein. In such an instance, theprocessor 218 may perform the feature or combination of features inconjunction with peripheral device interface 212, display interface 214,memory 220, communication interface 222, and/or data storage device 224.

Although FIG. 2 shows that the computing device 210 includes a singleprocessor 218, single memory device 220, single communication interface222, single peripheral device interface 212, single display deviceinterface 214, and single storage device 224, the computing device mayinclude multiples of each or any combination of these components and maybe configured to perform analogous functionality to that describedabove.

In the present invention, display interface 214 may be operable toprovide a display such as display 240 that are capable of displayingindicia representative of measured and calculated activity andphysiological parameters such as one or more of a activity level,calories burned, pulse rate, etc. User display 240 may also be capableof storing or displaying expected historical or activity trending datarelated to one or more of the measured data or parameters orcombinations of the measured data and parameters. For example, if auser's measured activity level deviates from an expected level, then theuser may receive a warning or other type of message on device 230.

Each or any combination of the components/modules shown in FIG. 2 may beimplemented as one or more software modules or objects, one or morespecific-purpose processor elements, or as combinations thereof.Suitable software modules include, by way of example, an executableprogram, a function, a method call, a procedure, a routine orsub-routine, one or more processor-executable instructions, an object,or a data structure. In addition or as an alternative to the features ofthese modules described above with reference to FIG. 2, these modulesmay perform functionality described later herein.

FIG. 3 shows an overall exemplary system architecture 300 that may beused for activity detection and analysis using sensor based data. Theexample architecture 300 includes a computer system 305 including a datasystem 310, a web system 320 and a business terminal 330, a network 340,and a plurality of mobile sensor computing devices 350 a-n associatedwith a plurality of users 352 a-n. Although, only two sensor computingdevices are shown, three or more sensor computing devices may beutilized in the present invention. Sensor device may includeelectro-mechanical type or MEMS type activity sensors that use one ormore accelerometers and microprocessors properly programmed to detectuser activity. These activity sensors generally have 1-, 2or 3-axisaccelerometers to measure accelerations and generate electronic signalscorresponding to physical movement of the users. The software in themicroprocessor then processes the electronic acceleration signals todetermine activity levels. Data system 310 may include a communicationsinterface 312, a rules processor 314, a user database 316 and anactivity information database 318.

In one embodiment, business rules processor 314 may include one or morerules and/or predictive models. The rules processor 314 may use the oneor more rules and/or predictive models to evaluate activity data as wellas customer data to determine, for example, if an exception conditionoccurs such as a potential fraudulent condition related to the activitylevel of one or more of the users 352 a-n. Generally, a predictive modeltakes into account a plurality of parameters, and in embodiments maytake into account any number of parameters, such as up to 10 parameters,up to 100 parameters or several hundred or more parameters. Thepredictive model may include one or more of neural networks, Bayesiannetworks (such as Hidden Markov models), expert systems, decision trees,collections of decision trees, support vector machines, or other systemsknown in the art for addressing problems with large numbers ofvariables. Preferably, the predictive model is trained on prior data andoutcomes. The specific data and outcomes analyzed vary depending on thedesired functionality of the particular predictive model. The particulardata parameters selected for analysis in the training process aredetermined by using regression analysis and/or other statisticaltechniques known in the art for identifying relevant variables inmultivariable systems.

In other embodiments, one or more decision trees, equations or tablesmay be included with and executed by rules processor 314. Decision treesmay include decisions relating to identified terms and phrases andequivalent terms and phrases, in accordance with text based analysisprinciples.

In one embodiment, data system 310, terminal 330, and remote device 350a-n are in communication via a network 340. Data system 310 shown inFIG. 3 is an embodiment of a system that might be implemented solelywithin a single location or be an aggregation of one or more othersubsystems including one or more partner, third party administratorand/or vendor subsystems to allow communications and data transferbetween the company and representatives, investigators, adjusters,customers, and agents. Data transferred through network 340 to system310 may pass through one or more firewalls or other security typecontrols implemented within web system 320 and/or in standalone devices.The firewall allows access to network 340 only through predeterminedconditions/ports. In another embodiment, the firewall restricts theInternet IP addresses that may access web system 320.

Referring to FIG. 3 still, rules processor 314 may include one or morebusiness rules and one or more predictive models, decision trees,equations and/or tables, in conjunction with one or more softwaremodules or objects and one or more specific-purpose processor elements,to perform the processing required by embodiments of the presentinvention such as for evaluating sensor data.

The user information database 316 may store information, data anddocuments that relate to customers such as group benefits information aswell as location information. In the present invention, locationinformation may also be used in conjunction with detected activityinformation to determine if the user is within permissible activitylevels. For example, if the user is at a location that requires activitylevels that are not within predicted or expected activity levels for theuser then the location may be indicative of an exepction condition.Activity information database 318 may store information, data anddocuments from user devices 350 a-n and remote devices 350 a-n. Userinformation database 316 and activity information database 318 may bespread across one or more computer-readable storage media, and may be orinclude one or more relational databases, hierarchical databases,object-oriented databases, one or more flat files, one or morespreadsheets, and/or one or more structured files. User informationdatabase 316 and activity information database 318 may be managed by oneor more database management systems (not depicted), which may be basedon a technology such as Microsoft SQL Server, MySQL, Oracle RelationalDatabase Management System (RDBMS), PostgreSQL, a NoSQL databasetechnology, and/or any other appropriate technology. Communicationbetween the data system 310 and the other elements in the examplearchitecture 300 of FIG. 3 may be performed via the communicationsinterface module 312.

Referring still to FIG. 3, web system 320 may provide a web interfacethat may be accessed directly by a user such as an insured, a claimsrepresentative, an adjuster and other third party entity employing userdevices 332 a-n to communicate and interact with a companyrepresentative employing terminal 330. In certain embodiments, terminal330 can include, but are not limited to cellular telephones, otherwireless communication devices, personal digital assistants, pagers,laptop computers, tablet computers, smartphones, other mobile displaydevices, or combinations thereof. In embodiments of the presentinvention, terminal 330 may communicate with the web site system 320that may be operated by or under the control of an entity or other thirdparty entity such as an outsourced type entity or third partyadministrator type entity.

The web site system 320 may include a web application module 322 and aHyperText Transfer Protocol (HTTP) server module 324. The webapplication module 322 may generate the web pages that make up the website and that are communicated by the HTTP server module 324. Webapplication module 322 may be implemented in and/or based on atechnology such as Active Server Pages (ASP), PHP: HypertextPreprocessor (PHP), Python/Zope, Ruby, any server-side scriptinglanguage, and/or any other appropriate technology.

The HTTP server module 324 may implement the HTTP protocol, and maycommunicate HyperText Markup Language (HTML) pages and related data fromthe web site to/from client devices terminal 334, user device 350 c,using HTTP. The HTTP server module 324 may be, for example, a Sun-ONEWeb Server, an Apache HTTP server, a Microsoft Internet InformationServices (IIS) server, and/or may be based on any other appropriate HTTPserver technology. The web site system 320 may also include one or moreadditional components or modules (not depicted), such as one or moreswitches, load balancers, firewall devices, routers, and devices thathandle power backup and data redundancy.

Referring still to FIG. 3, terminal 330 may include a web browser module334, which may communicate data related to the web site to/from the HTTPserver module 324 and the web application module 322 in the web sitesystem 320. The web browser module 334 may include and/or communicatewith one or more sub-modules that perform functionality such asrendering HTML (including but not limited to HTML5), rendering rasterand/or vector graphics, executing JavaScript, and/or renderingmultimedia content. Alternatively or additionally, the web browsermodule 334 may implement Rich Internet Application (RIA) and/ormultimedia technologies such as Adobe Flash, Microsoft Silverlight,and/or other technologies. The web browser module 334 may implement RIAand/or multimedia technologies using one or web browser plug-in modules(such as, for example, an Adobe Flash or Microsoft Silverlight plugin),and/or using one or more sub-modules within the web browser module 334itself

The example architecture 300 of FIG. 3 may also include one or morewired and/or wireless networks via which communications between theelements and components shown in the example architecture 300 may takeplace. The networks may be private or public networks, cloud or sharednetworks and/or may include the Internet. In one embodiment, thousandsand even millions of users 352 a-n are simultaneously transmittingactivity data via devices 350 a-n via network 340 for processing viadata system 310.

Referring to FIG. 4, an exemplary computer system 400 for use in animplementation of the invention will now be described. Computer system400 may be configured to perform pricing, risk management, loss controlservices and claims evaluation and management for one or more companiesand their associated agents, personnel, customers and staff usingdevices 402. System 400 may include device 402, which may be a companyagent or vendor terminal or device, a network 404, a processing and datasystem 406 and one or more third party servers 408 and one or moreactivity sensors 409. In embodiments of the present invention,processing and data system 406 is responsible for the processing of useractivity and motion data to adjust pricing parameters associated with agroup benefits offering. Third party servers may be administered bythird party web operators or social media server operators.

In processing and data system 406, a central processing unit orprocessor 410 executes instructions contained in programs such as abenefits management application program 414, stored in storage devices420. Processor 410 may provide the central processing unit (CPU)functions of a computing device on one or more integrated circuits. Asused herein, the term “processor” broadly refers to and is not limitedto a single- or multi-core general purpose processor, a special purposeprocessor, a conventional processor, a Graphics Processing Unit (GPU), adigital signal processor (DSP), a plurality of microprocessors, one ormore microprocessors in association with a DSP core, a controller, amicrocontroller, one or more Application Specific Integrated Circuits(ASICs), one or more Field Programmable Gate Array (FPGA) circuits, anyother type of integrated circuit (IC), a system-on-a-chip (SOC), and/ora state machine.

Storage devices 420 may include suitable media, such as optical ormagnetic disks, fixed disks with magnetic storage (hard drives), tapesaccessed by tape drives, and other storage media. Processor 410communicates, such as through bus 411 and/or other data channels, withcommunications interface unit 412, storage devices 420, system memory430, and input/output controller 440. System memory 430 may furtherinclude non-transitory computer-readable media such as a random accessmemory 432 and a read only memory 434. Random access memory 432 maystore instructions in the form of computer code provided by application414 to implement embodiments of the present invention. One or morecomputer programs may be stored in memory, or computer usable media,such as storage devices 420 and random access memory 432, in the form ofcomputer readable program code adapted to be executed by at least oneprocessor, such as a central processing unit 410. The one or morecomputer programs may include instructions for performing steps ofmethods of embodiments of the invention described herein. System 400further includes an input/output controller 440 that may communicatewith processor 410 to receive data from user inputs such as pointingdevices, touch screens, and audio inputs, and may provide data tooutputs, such as data to video drivers for formatting on displays, anddata to audio devices.

Storage devices 420 are configured to exchange data with processor 410,and may store programs containing processor-executable instructions, andvalues of variables for use by such programs. Processor 410 isconfigured to access data from storage devices 420, which may includeconnecting to storage devices 420 and obtaining data or reading datafrom the storage devices, or placing data into the storage devices.Storage devices 420 may include local and network accessible massstorage devices. Storage devices 420 may include media for storingoperating system 422 and mass storage devices such as storage 424 forstoring data related to sensor data, including sensor activityinformation, GIS data and other location based data, policy dataincluding location data, such as physical address data, and address datasuch as telephone number data and e-mail address data, predictive modeldata, and user related data.

Communications interface unit 412 may communicate via network 404 withother computer systems such as third party servers 408 as well as otherinternal and external servers, computer systems of remote sources ofdata, and with systems for implementing instructions output by processor410. Processing and data system 406 may also be configured in adistributed architecture, wherein databases, data storage devices andprocessors are housed in separate units or locations. The serversperform primary processing functions and contain at a minimum, a RAM, aROM, and a general controller or processor. In such an embodiment, eachof these servers is attached to a communications hub or port that servesas a primary communication link with other servers, client or usercomputers and other related devices. The communications hub or port mayhave minimal processing capability itself, serving primarily as acommunications router. A variety of communications protocols may be partof the system, including but not limited to: Ethernet, SAP, SASTM, ATP,Bluetooth, GSM and TCP/IP. Network 404 may be or include wired orwireless local area networks and wide area networks, and overcommunications between networks, including over the Internet.

One or more public cloud, private cloud, hybrid cloud and cloud-likenetworks may also be implemented, for example, to handle and conductprocessing of one or more transactions or processing of embodiments ofthe present invention. Cloud based computing may be used herein tohandle any one or more of the application, storage and connectivityrequirements of embodiments of the present invention. For example one ormore private clouds may be implemented to store, process and otherwisehandle sensor data and discount data of embodiments of the presentinvention. Furthermore, any suitable data and communication protocolsmay be employed to accomplish the teachings of embodiments of thepresent invention.

FIG. 5 illustrates an exemplary computerized method 500 of the presentinvention. In one embodiment, the method 500 includes the step capturingsensor data, step 510. Sensor data may be captured from one or moreusers 512 that are utilizing one or more MEMS sensor devices 514. Themethod further continues with synchronizing sensor data with useridentifications, step 520. The method continues with validating andstoring sensor data in a database, step 530. The method continues withcorrelating sensor data with predicted activity data, step 540. Thesystem determines if the sensor data is within range of predictedactivity data, step 550. For example, if a user's very vigorous activityis correlated with predicted sedentary activity then an exceptioncondition may be determined. Accordingly, the system transmitsinstructions to the sensor device, step 560. For example, if the dataincludes an indication of a deviation, violation or a breach of athreshold, information may be sent to the sensor to capture additionalinformation or data. Data may also indicate that the usr is adhering toa program of controlled activity. The data may be also indicate that theuser is capable of resuming customary activity. Additionally, thechanges in data such as deviations of data may pre-emptively indicatethat the user may be experiencing a change in circumstances orconditions that may be indicative of an upcoming condition or resolutionof a condition. A communication to the user 512 may also be sent toverify their activity level, such as via an electronic text message orelectronic mail message.

FIG. 6 shows an exemplary database structure 600 that may be implementedas an electronic database as described herein to support transactionsrelated to user activity level sensor data. Database structure 600 maybe implemented as an analytic, management, operational, flat-file,rational, or hierarchical database in a standalone, network, ordistributed configuration or as part of a database management system(DBMS) that interact with the user, other applications, and the databaseitself to capture and analyze data for use in loss control andpreemptive claim management such as MySQL, MariaDB, PostgreSQL, SQLite,Microsoft SQL Server, Oracle, SAP, dBASE, FoxPro, IBM DB2, LibreOfficeBase and FileMaker Pro. Database 600 includes a number of databasecolumn fields 610, 620, 630, and 640 and a number of database rows 660,662, 664, and 666. Column fields 610, 620, 630, and 640 may correspondto one or more fields such as Activity Level 610, Benefit Categorization620, Activity Examples 630, and Reference Levels 640. In database 600, acertain activity type 670 may be associated with a certain benefitcategorization 672, along with certain activity examples 674, andreference activity level data 676. For example, a short term benefitcategorization may be associated with a moderate reference level ofactivity or caloric exertion such that if a user has a long term benefitcategorization associated with them but the MEMS sensors determine thatthe activity level corresponds to more of a short term benefitcategorization, then this may be determined to be an exception conditionthat may require the sensors to increase their sampling rates andfrequencies. In the present invention, detected activity levels fromMEMS based sensors may be correlated to database 600 in order todetermine if the detected activity level matches or fall within rangessuch as within column 640. If a user's activity level continuallymatches or falls within ranges then the user may be provided certainbenefits or conversely if the user's level continually exceeds a range,the user may be monitored more on an active basis.

FIG. 7 illustrates another exemplary computerized method 700 of thepresent invention. In one embodiment, the method 700 includes the stepof initiating an activity monitoring session, step 710. An activitysensor is then activated for a predetermined period, step 720. Data isreceived from one or more sensors during a monitoring period, step 730.It is determined if the sensor data is outside an accepted or approvedactivity level range associated with a benefit, step 740. For example, ashort term benefit may be associated with medium level of activitywhereas a long term benefit may be associated with minimal levels ofactivity. The process continues with an electronic communication beingissued to the monitored user to validate an activity level, step 750. Aconditional adjustment of user parameters or data is then made, 760.User or parameters data may be a pricing parameter adjustments such as adiscount or a surcharge associated with the user benefit. Parameters mayalso include an initial discount for simply providing the activity datasuch as in step 720 and/or a differential discount based on the amountof data provided or received such as during step 730 and/or thecorrelation of the data with historical and/or predicted activity data.In the present invention, the user may also be provided certain deviceor equipment, increased level of related services, recognition typeinformation or items, or certain monetary benefits related to theircondition as an incentive. Pricing may also affect a group or users aswell as individual users.

FIG. 8 illustrates an exemplary system implementation 800 of the presentinvention that includes a portable computing device 810 that is enabledto capture and transmit activity related signals 820 and data from auser 830. Portable computing device 810 includes one or more sensorsthat provide activity information and data 820 about the user 830 via anetwork 850 to a third party server or mainframe 860 and a remotecomputing device 870. Third party server or mainframe 860 may issuesensor instructions 880 and device 870 may also provide instructions orinterrogate sensors 890 via network 850. Sensor instruction may includefor example, an instruction to increase a sampling rate of the sensordevice. It is contemplated that other instructions such as increasing,decreasing, starting or stopping sampling may be issued too. Inoperation, computing device 810 can detect various activity based eventsand determine an activity state of a user. In some operational modes anoccurrence of an activity level can initiate a transmission of processeddata to generate an audio and/or text based message to device 870.Device 870 can process received activity data to determine theoccurrence of certain activity conditions or exceptions such as when ameasured activity level 820 breaches a certain reference activity levelsuch as displayed on device 870. According to various embodiments of theinvention, device 870 may be configured to display charts, graphs orother visual representations of the monitored activity data. Accordingto an embodiment of the invention device 870 is capable of retrievingsupplemental information related to the user, and displaying theretrieved user information. In the present invention, a deviation orother curve fit comparison may be performed to determine a magnitude ofdifference between the measured activity information 820 and thereference activity information which is associated with a certainbenefit for the user. Various thresholds and/or limits may be set suchas via server 860 to determine if the measured activity informationdiffers from the reference activity information.

Although the methods and features described above with reference toFIGS. 1-8 are described above as performed using the examplearchitecture 100 of FIG. 1 and the exemplary system 200 of FIG. 2, themethods and features described above may be performed using anyappropriate architecture and/or computing environment. Although featuresand elements are described above in particular combinations, eachfeature or element can be used alone or in any combination with orwithout the other features and elements. For example, each feature orelement as described with reference to FIGS. 1-8 may be used alonewithout the other features and elements or in various combinations withor without other features and elements. Sub-elements of the methods andfeatures described above with reference to FIGS. 1-8 may be performed inany arbitrary order (including concurrently), in any combination orsub-combination.

What is claimed is:
 1. A Micro-Electro-Mechanical-System (“MEMS”) basedmethod for detecting and analyzing activity levels, comprising:collecting, by a wearable MEMS sensor, user activity data of a userwearing the MEMS sensor; wirelessly transmitting, from a transceiver ofthe wearable MEMS sensor to a mobile electronic device, the collecteduser activity data, wherein the mobile electronic device is adapted todisplay the user activity data; forwarding, from the mobile electronicdevice to a data adapter of a remote server system device via a wirelessrouter, the collected user activity data; analyzing, by the remoteserver system device, the user activity data to determine an associatedactivity classification, each activity classification corresponding to apre-determined condition and an associated condition benefit;automatically generating, by a computer processor of the remote serversystem device, an exception condition for an investigator upon detectionof a potential fraudulent condition based on a comparison of the senseduser activity data with a historical profile of that particular user'spast activities and a predictive model; and transmitting instructions,from the mobile electronic device to the MEMS sensor, to vary a samplingcondition of the MEMS sensor in response to the determined activityclassification.
 2. The method of claim 1, wherein the wearable MEMSsensor includes a nonvolatile memory having stored representations ofinstructions to electronically correlate the user activity data with areference scale of activity levels.
 3. The method of claim 1, whereinthe mobile electronic device includes an accelerometer and a memoryhaving stored instructions, and a processor circuit.
 4. The method ofclaim 1, wherein the wearable MEMS sensor comprises at least a pluralityof accelerometers capturing representations of actual user activity thatare indicative of a potential fraudulent condition related to at leastone of a short term disability or a long term disability.
 5. The methodof claim 1, wherein the MEMS sensor generates an output signalindicative of an activity level that corresponds to a certain usercondition.
 6. The method of claim 5, wherein location data is used toinfer an activity level.
 7. The method of claim 6, wherein the remoteserver system device outputs an electronic communication to the user inthe event the user condition does not correspond to the detectedactivity level.
 8. The method of claim 1, where the mobile electronicdevice electronically correlates the user activity levels with apredetermined classification of allowable activity levels, the userbeing provided a differential discount.
 9. The method of claim 1,wherein the mobile electronic device includes a processor circuit and avolatile memory and the processor circuit is operable to store into thevolatile memory a time-window of data responsive to activity of theuser, wherein the activity may qualify the user for a benefit.
 10. Themethod of claim 9, wherein the benefit is a pricing adjustment.
 11. Themethod of claim 1, wherein the MEMS sensor includes a processor and amemory, wherein and the processor is configured to generate an activityclassification of the user activity.
 12. The method of claim 1, whereinthe activity classification qualifies the user for a pricing adjustment.13. The method of claim 1, wherein the predictive model is associatedwith at least one of: (i) a neural network, (ii) a Bayesian network,(iii) a hidden Markov model, (iv) an expert system, (v) a decision tree,(vi) a collection of decision trees, and (vii) a support vector machine.14. A Micro-Electro-Mechanical-System (“MEMS”) sensor based method formeasuring activity levels, comprising: measuring, by at least onewearable MEMS sensor configured within a mobile electronic device, useractivity data, the MEM sensor including a wireless communicationtransceiver internal to the MEMS sensor for transmitting the useractivity data; receiving, by a remote server system device via acommunications network, the user activity data; analyzing, by the remoteserver system device, the user activity data to determine an associatedactivity classification associated with the user activity data, eachactivity classification corresponding to a predetermined condition andan associated condition benefit; automatically generating, by the remoteserver system device, an exception condition for an investigator upondetection of a potential fraudulent condition based on a comparison ofthe sensed user activity data with a historical profile of thatparticular user's past activities and a predictive model; anddetermining, based on the determined activity classification, a contentof one or more electronic communications transmitted by the remoteserver system device to a user device.
 15. The method of claim 14,wherein the electronic communication is configured for display on themobile electronic device.
 16. The method of claim 15, wherein thedetermined activity classification is based on historical analysis ofactivity levels associated with a variety of conditions.
 17. The methodof claim 16, wherein the conditions relate to a sedentary or an activecondition that are correlated with a compliant and a non-compliantclassification.
 18. A Micro-Electro-Mechanical-System (“MEMS”) basedmethod for detecting and analyzing activity levels, comprising:collecting, by a wearable MEMS sensor, user activity data of a userwearing the MEMS sensor; wirelessly transmitting, from a transceiver ofthe wearable MEMS sensor to a mobile electronic device, the collecteduser activity data, wherein the mobile electronic device is adapted todisplay the user activity data; forwarding, from the mobile electronicdevice to a data adapter of a remote server system device via a wirelessrouter, the collected user activity data; analyzing, by the remoteserver system device, the user activity data to determine an associatedactivity classification, each activity classification corresponding to apre-determined condition and an associated condition benefit; andautomatically generating, by a computer processor of the remote serversystem device, an exception condition for an investigator upon detectionof a potential fraudulent condition based on a comparison of the senseduser activity data with a historical profile of that particular user'spast activities and a predictive model.
 19. The method of claim 18,wherein the wearable MEMS sensor includes a nonvolatile memory havingstored representations of instructions to electronically correlate theuser activity data with a reference scale of activity levels.
 20. Themethod of claim 19, wherein the mobile electronic device includes anaccelerometer including a memory having stored instructions, and aprocessor circuit.
 21. The method of claim 20, further comprising:transmitting instructions, from the mobile electronic device to the MEMSsensor, to vary a sampling condition of the MEMS sensor in response tothe determined activity classification.